Como impactó la Nueva Ley de Alquileres en el Mercado Inmobiliario
Bardoneschi Gonzalo - Bernassar Felicitas - Cornudet Victoria - Mayochi Pablo
26/6/2021
En el presente trabajo nos proponemos analizar como impactó el aumento de los precios en el mercado de alquileres a partir de la nueva ley de alquileres (Ley 27.551 del Código Civil y Comercial de la Nación) promulgada el 30 de junio de 2020.
Importamos el dataset de Properati para analizar la evolución de los precios
library(tidyverse)
library(sf)
library(leaflet)
library(ggmap)
library(lubridate)
library(dplyr)
library(osmdata)googleapi_data_properati <- vroom::vroom("https://storage.googleapis.com/properati-data-public/ar_properties.csv.gz")Veamos sus columnas y valores resumidos
summary(googleapi_data_properati)## id ad_type start_date
## Length:1000000 Length:1000000 Min. :2020-05-24
## Class :character Class :character 1st Qu.:2020-08-28
## Mode :character Mode :character Median :2020-11-14
## Mean :2020-11-29
## 3rd Qu.:2021-03-11
## Max. :2021-06-17
##
## end_date created_on lat lon
## Min. :2020-05-24 Min. :2020-05-24 Min. :-54.84 Min. :-122.54
## 1st Qu.:2020-10-25 1st Qu.:2020-08-28 1st Qu.:-34.72 1st Qu.: -58.88
## Median :2021-04-05 Median :2020-11-14 Median :-34.59 Median : -58.49
## Mean :3688-04-29 Mean :2020-11-29 Mean :-34.38 Mean : -59.49
## 3rd Qu.:2021-06-30 3rd Qu.:2021-03-11 3rd Qu.:-34.42 3rd Qu.: -58.40
## Max. :9999-12-31 Max. :2021-06-17 Max. : 49.63 Max. : 180.00
## NA's :105767 NA's :105809
## l1 l2 l3 l4
## Length:1000000 Length:1000000 Length:1000000 Length:1000000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## l5 l6 rooms bedrooms
## Length:1000000 Mode:logical Min. : 1.00 Min. :-16.0
## Class :character NA's:1000000 1st Qu.: 2.00 1st Qu.: 1.0
## Mode :character Median : 3.00 Median : 2.0
## Mean : 2.92 Mean : 2.1
## 3rd Qu.: 4.00 3rd Qu.: 3.0
## Max. :40.00 Max. :900.0
## NA's :285821 NA's :350067
## bathrooms surface_total surface_covered price
## Min. : 1.00 Min. : -136.0 Min. : -130 Min. :0.000e+00
## 1st Qu.: 1.00 1st Qu.: 50.0 1st Qu.: 45 1st Qu.:3.800e+04
## Median : 1.00 Median : 95.0 Median : 78 Median :9.700e+04
## Mean : 1.71 Mean : 513.8 Mean : 11656 Mean :3.461e+05
## 3rd Qu.: 2.00 3rd Qu.: 250.0 3rd Qu.: 168 3rd Qu.:2.100e+05
## Max. :20.00 Max. :200000.0 Max. :2147483647 Max. :3.100e+09
## NA's :234878 NA's :522169 NA's :512244 NA's :41757
## currency price_period title description
## Length:1000000 Length:1000000 Length:1000000 Length:1000000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## property_type operation_type
## Length:1000000 Length:1000000
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## Procederemos a un plan de limpieza y análisis de datos:
- Filtrado de registros no relevantes
- Publicaciones sin precio
- Sin moneda de cotización
- Sin Latitud y longitud
- Tipos de propiedades no destinadas a uso de vivienda (Oficinas, garages, entre otros)
- Para las publicaciones asignar su precio en dólares según la cotización de la fecha
- Reconversión de la variable FECHA a “Mes - Año”
Hay bastantes columnas y valores por limpiar. Varias columnas no son de nuestro interés, como Título y descripción de la publicación, así como tampoco nos interesan los valores sin ubicación (latitud y longitud) así como las publicaciones sin precio. Adicionalmente, nos interesa quedarnos con un dataset de Argentina, y los tipos de propiedad para vivienda.
clean_data_properati <- filter(googleapi_data_properati, !is.na(lat), !is.na(lon), !is.na(price), !is.na(currency), l1=="Argentina", !price_period=="Semanal", !currency=="COP", property_type=="Departamento" | property_type=="PH" | property_type=="Casa")
clean_data_properati <- select(clean_data_properati, !title & !description & !id & !ad_type)
clean_data_properati <- clean_data_properati %>% mutate(operation_type = case_when(operation_type == "Alquiler temporal" ~ "Alquiler", operation_type == "Alquiler" ~ "Alquiler", operation_type == "Venta" ~ "Venta"))Vemos que el dataset bajó considerablemente. Ahora tenemos que convertir las variables de caracteres en factores para poder analizarlos correctamente.
clean_data_properati <- clean_data_properati %>% mutate_if(is.character,as.factor)
summary(clean_data_properati)## start_date end_date created_on
## Min. :2020-05-24 Min. :2020-05-24 Min. :2020-05-24
## 1st Qu.:2020-09-19 1st Qu.:2020-10-29 1st Qu.:2020-09-19
## Median :2020-12-08 Median :2021-04-13 Median :2020-12-08
## Mean :2020-12-14 Mean :3058-06-13 Mean :2020-12-14
## 3rd Qu.:2021-03-23 3rd Qu.:2021-06-05 3rd Qu.:2021-03-23
## Max. :2021-06-17 Max. :9999-12-31 Max. :2021-06-17
##
## lat lon l1
## Min. :-54.83 Min. :-101.11 Argentina:253430
## 1st Qu.:-34.63 1st Qu.: -58.74
## Median :-34.58 Median : -58.48
## Mean :-34.31 Mean : -59.33
## 3rd Qu.:-34.44 3rd Qu.: -58.41
## Max. : 23.13 Max. : -54.13
##
## l2 l3 l4
## Capital Federal :99523 Palermo : 18627 Nordelta : 5382
## Bs.As. G.B.A. Zona Norte:52705 Rosario : 18384 La Plata : 4631
## Bs.As. G.B.A. Zona Sur :28226 Tigre : 15973 Olivos : 2660
## Santa Fe :21283 Belgrano: 14610 Nueva Córdoba : 2589
## Bs.As. G.B.A. Zona Oeste:15547 Córdoba : 9868 Palermo Hollywood: 2498
## Córdoba :14810 (Other) :171612 (Other) : 65535
## (Other) :21336 NA's : 4356 NA's :170135
## l5 l6 rooms bedrooms
## BarrioPortezuelo : 425 Mode:logical Min. : 1.00 Min. : 0.00
## Barrio Los Alisos: 294 NA's:253430 1st Qu.: 2.00 1st Qu.: 1.00
## Barrio El Golf : 289 Median : 3.00 Median : 2.00
## Barrio Los Sauces: 167 Mean : 3.06 Mean : 2.21
## Barrio La Alameda: 153 3rd Qu.: 4.00 3rd Qu.: 3.00
## (Other) : 892 Max. :40.00 Max. :153.00
## NA's :251210 NA's :33266 NA's :37246
## bathrooms surface_total surface_covered price
## Min. : 1.000 Min. : -136.0 Min. : -130 Min. :0.000e+00
## 1st Qu.: 1.000 1st Qu.: 47.0 1st Qu.: 44 1st Qu.:4.000e+04
## Median : 1.000 Median : 75.0 Median : 70 Median :1.050e+05
## Mean : 1.676 Mean : 156.4 Mean : 1375 Mean :3.139e+05
## 3rd Qu.: 2.000 3rd Qu.: 151.0 3rd Qu.: 139 3rd Qu.:2.150e+05
## Max. :20.000 Max. :175000.0 Max. :111111111 Max. :3.100e+09
## NA's :13754 NA's :75678 NA's :58242
## currency price_period property_type operation_type
## ARS: 64507 Mensual:253430 Casa : 74926 Alquiler: 76585
## USD:188923 Departamento:164203 Venta :176845
## PH : 14301
##
##
##
## Tenemos que llevar las publicaciones a una misma unidad de medida monetaria, sea en pesos o en dólares, para poder compararlas por barrio y en el tiempo.
dolar_bcra <- readxl::read_xlsx("DOLAR.BCRA.xlsx")Adjuntemos la cotización que tenia el dolar en cada fecha para la publicación del inmueble en ese día
unif_data_properati <- left_join(clean_data_properati, dolar_bcra, by=c("created_on"="Fecha"))summary(unif_data_properati)## start_date end_date created_on
## Min. :2020-05-24 Min. :2020-05-24 Min. :2020-05-24 00:00:00
## 1st Qu.:2020-09-19 1st Qu.:2020-10-29 1st Qu.:2020-09-19 00:00:00
## Median :2020-12-08 Median :2021-04-13 Median :2020-12-08 00:00:00
## Mean :2020-12-14 Mean :3058-06-13 Mean :2020-12-14 03:56:38
## 3rd Qu.:2021-03-23 3rd Qu.:2021-06-05 3rd Qu.:2021-03-23 00:00:00
## Max. :2021-06-17 Max. :9999-12-31 Max. :2021-06-17 00:00:00
##
## lat lon l1
## Min. :-54.83 Min. :-101.11 Argentina:253430
## 1st Qu.:-34.63 1st Qu.: -58.74
## Median :-34.58 Median : -58.48
## Mean :-34.31 Mean : -59.33
## 3rd Qu.:-34.44 3rd Qu.: -58.41
## Max. : 23.13 Max. : -54.13
##
## l2 l3 l4
## Capital Federal :99523 Palermo : 18627 Nordelta : 5382
## Bs.As. G.B.A. Zona Norte:52705 Rosario : 18384 La Plata : 4631
## Bs.As. G.B.A. Zona Sur :28226 Tigre : 15973 Olivos : 2660
## Santa Fe :21283 Belgrano: 14610 Nueva Córdoba : 2589
## Bs.As. G.B.A. Zona Oeste:15547 Córdoba : 9868 Palermo Hollywood: 2498
## Córdoba :14810 (Other) :171612 (Other) : 65535
## (Other) :21336 NA's : 4356 NA's :170135
## l5 l6 rooms bedrooms
## BarrioPortezuelo : 425 Mode:logical Min. : 1.00 Min. : 0.00
## Barrio Los Alisos: 294 NA's:253430 1st Qu.: 2.00 1st Qu.: 1.00
## Barrio El Golf : 289 Median : 3.00 Median : 2.00
## Barrio Los Sauces: 167 Mean : 3.06 Mean : 2.21
## Barrio La Alameda: 153 3rd Qu.: 4.00 3rd Qu.: 3.00
## (Other) : 892 Max. :40.00 Max. :153.00
## NA's :251210 NA's :33266 NA's :37246
## bathrooms surface_total surface_covered price
## Min. : 1.000 Min. : -136.0 Min. : -130 Min. :0.000e+00
## 1st Qu.: 1.000 1st Qu.: 47.0 1st Qu.: 44 1st Qu.:4.000e+04
## Median : 1.000 Median : 75.0 Median : 70 Median :1.050e+05
## Mean : 1.676 Mean : 156.4 Mean : 1375 Mean :3.139e+05
## 3rd Qu.: 2.000 3rd Qu.: 151.0 3rd Qu.: 139 3rd Qu.:2.150e+05
## Max. :20.000 Max. :175000.0 Max. :111111111 Max. :3.100e+09
## NA's :13754 NA's :75678 NA's :58242
## currency price_period property_type operation_type
## ARS: 64507 Mensual:253430 Casa : 74926 Alquiler: 76585
## USD:188923 Departamento:164203 Venta :176845
## PH : 14301
##
##
##
##
## Tipo de Cambio de Referencia - en Pesos - por Dólar
## Min. :68.26
## 1st Qu.:75.32
## Median :81.65
## Mean :82.72
## 3rd Qu.:91.36
## Max. :95.35
## NA's :45097Como a partir del 2020 ya en Argentina existía el recargo del 30% y del 35% a la compra de dólares, empujando la brecha cambiaria del dolar “blue”, y considerando que los precios de las propiedades por el efecto especulativo se calcularon históricamente en base al dolar “blue”, debiéramos tener en cuenta ese recargo del 65% para el tipo de cambio
unif_data_properati <- unif_data_properati %>%
rename("Cotiz_dolar"="Tipo de Cambio de Referencia - en Pesos - por Dólar")arg_data_properati <- unif_data_properati %>% mutate(Cotiz_dolar=Cotiz_dolar*1.65)
summary(arg_data_properati$Cotiz_dolar)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 112.6 124.3 134.7 136.5 150.7 157.3 45097Vemos que tenemos algunos NA, eso se debe a que la fecha de publicación de la propiedad fue un sábado, domingo, o feriado, por lo que el BCRA no emitió cotización para ese día en su informe. Tendremos que colocar la cotización del día más cercano
arg_data_properati %>% arrange(created_on, desc())## # A tibble: 253,430 x 22
## start_date end_date created_on lat lon l1 l2 l3 l4
## <date> <date> <dttm> <dbl> <dbl> <fct> <fct> <fct> <fct>
## 1 2020-05-24 2020-06-25 2020-05-24 00:00:00 -34.9 -58.6 Arge~ Bs.A~ Ezei~ Coun~
## 2 2020-05-24 2020-05-30 2020-05-24 00:00:00 -32.6 -60.2 Arge~ Entr~ Vict~ <NA>
## 3 2020-05-24 2020-11-26 2020-05-24 00:00:00 -34.6 -58.4 Arge~ Capi~ Pale~ Pale~
## 4 2020-05-24 2020-06-25 2020-05-24 00:00:00 -34.6 -58.4 Arge~ Capi~ Pale~ Pale~
## 5 2020-05-24 2020-06-15 2020-05-24 00:00:00 -34.6 -58.4 Arge~ Capi~ Pale~ Pale~
## 6 2020-05-24 2020-07-21 2020-05-24 00:00:00 -32.9 -60.7 Arge~ Sant~ Rosa~ <NA>
## 7 2020-05-24 2020-06-11 2020-05-24 00:00:00 -32.9 -60.6 Arge~ Sant~ Rosa~ <NA>
## 8 2020-05-24 2020-05-27 2020-05-24 00:00:00 -33.0 -60.6 Arge~ Sant~ Rosa~ <NA>
## 9 2020-05-24 2020-06-15 2020-05-24 00:00:00 -34.6 -58.4 Arge~ Capi~ Belg~ <NA>
## 10 2020-05-24 2020-06-15 2020-05-24 00:00:00 -34.9 -58.0 Arge~ Bs.A~ La P~ La P~
## # ... with 253,420 more rows, and 13 more variables: l5 <fct>, l6 <lgl>,
## # rooms <dbl>, bedrooms <dbl>, bathrooms <dbl>, surface_total <dbl>,
## # surface_covered <dbl>, price <dbl>, currency <fct>, price_period <fct>,
## # property_type <fct>, operation_type <fct>, Cotiz_dolar <dbl>arg_data_properati <- arg_data_properati %>%
fill(Cotiz_dolar)
arg_data_properati <- arg_data_properati %>%
fill(Cotiz_dolar, .direction = "downup")
summary(arg_data_properati$Cotiz_dolar)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 112.6 123.5 135.5 136.2 150.5 157.3Llevamos todos los precios a pesos argentinos para trabajar en la misma unidad de medida y poder comparar
arg_data_properati <- arg_data_properati %>% mutate(precio = case_when(currency == "USD" ~ price*Cotiz_dolar, currency == "ARS" ~ price))
summary(arg_data_properati$precio)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000e+00 1.724e+05 1.331e+07 2.242e+07 2.764e+07 3.714e+09options(scipen = 10)
ggplot(data = arg_data_properati) + geom_histogram(aes(x = precio))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Generamos la variable de ‘precioxm2’ que nos servirá para futuros análisis en distintas ciudades
summary(arg_data_properati$surface_covered)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -130 44 70 1375 139 111111111 58242arg_data_properati$surface_covered[is.na(arg_data_properati$surface_covered)] <- mean(arg_data_properati$surface_covered,na.rm=TRUE)
summary(arg_data_properati$surface_covered)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -130 50 100 1375 385 111111111arg_data_properati <- arg_data_properati %>% filter(surface_covered>0)
summary(arg_data_properati$surface_covered)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 50 100 1375 385 111111111arg_data_properati <- arg_data_properati %>% mutate(precioxm2 = precio/surface_covered)
summary(arg_data_properati$precioxm2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1200 129715 188993 289848 88558833Analizamos la evolución de la variable
class(arg_data_properati$created_on)## [1] "POSIXct" "POSIXt"arg_data_properati <- arg_data_properati %>%
mutate(fecha=as.Date(as.character(as.POSIXct(created_on))))class(arg_data_properati$fecha)## [1] "Date"Para un análisis más agregado, necesitamos verlo por año y mes. Realizamos un gráfico “estándar” que nos servirá para cuando subdividamos el análisis.
arg_data_properati <- arg_data_properati %>%
mutate(año_mes = str_sub(fecha, 1 , 7)) %>%
mutate(fecha = ymd(glue::glue("{año_mes}-01")))
arg_data_properati %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+300000, label=precioprom), size=5)+
labs(title = "Evolución de la variable precio - Prueba de gráfico base",
subtitle = "Fuentes: PROPERATI")
Ya tenemos la variable fecha, y los precios comparables en todos los registros. Continuando, nos aseguramos que tenemos los datos de Argentina, sin filtraciones
geo_arg_data_properati <- st_as_sf(arg_data_properati,coords=c("lon","lat"), crs=4326)ggplot(geo_arg_data_properati)+
geom_sf() +
labs(title = "Mapa base",
subtitle = "Fuentes: Properati")
Vemos que tenemos un outlier. Deberemos filtrarlo y eliminarlo de nuestro dataset
limites_arg <- read_sf("pais.shp")
limites_arg <- st_transform(limites_arg, 4326)
geo_arg_data_properati <- mutate(st_join(geo_arg_data_properati, limites_arg))## although coordinates are longitude/latitude, st_intersects assumes that they are planar## Warning in st_is_longlat(x): bounding box has potentially an invalid value range
## for longlat data## although coordinates are longitude/latitude, st_intersects assumes that they are planargeo_arg_data_properati <- filter(geo_arg_data_properati, !is.na(entidad))
ggplot(geo_arg_data_properati)+
geom_sf() +
labs(title = "Mapa base",
subtitle = "Fuentes: Properati")
Ahora dividimos nuestro dataset entre las publicaciones que corresponden a Capital Federal y aquellas que no
data_properati_non_CABA <- filter(geo_arg_data_properati, !l2=="Capital Federal")
data_properati_CABA <- filter(geo_arg_data_properati, l2=="Capital Federal")Finalmente tenemos el dataset con el que queremos trabajar nuestros análisis. Sin embargo, tenemos que mapearlo primero para asegurarnos de ello.
barrios <- read_sf("barrios.geojson")
barrios <- st_transform(barrios, 4326)ggplot()+
geom_sf(data=barrios) +
geom_sf(data=data_properati_CABA) +
theme_minimal() +
theme(legend.title=element_text(size=8))+
labs(title = "Mapa base",
subtitle = "Fuente: Properati")
Haremos un join espacial con el dataset de Barrios para quedarnos con las propiedades de la Ciudad Autónoma de Buenos Aires
data_properati_CABA <- mutate(st_join(data_properati_CABA, barrios))## although coordinates are longitude/latitude, st_intersects assumes that they are planar
## although coordinates are longitude/latitude, st_intersects assumes that they are planardata_properati_CABA <- filter(data_properati_CABA, !is.na(barrio))Mapeamos nuevamente
ggplot()+
geom_sf(data=barrios) +
geom_sf(data=data_properati_CABA) +
theme_minimal() +
theme(legend.title=element_text(size=8))+
coord_sf(datum = NA)+
labs(title = "Mapa base",
subtitle = "Fuente: Properati")
Nos será conveniente dividir el dataset en dos. Por un lado venta, y por otro lado alquileres. Fundamentalmente esto se debe a que los Alquileres se publican en pesos y las Ventas de inmuebles en dólares.
data_properati_CABA_venta <- filter(data_properati_CABA, operation_type == "Venta")
data_properati_CABA_alq <- filter(data_properati_CABA, !operation_type == "Venta")ggplot(data = data_properati_CABA_venta) + geom_histogram(aes(x = precio))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = data_properati_CABA_alq) + geom_histogram(aes(x = precio))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Ahora procederemos a realizar los gráficos y análisis correspondientes para responder nuestros interrogantes de investigación
Evolución de Capital Federal
Gráfico de líneas con el precio promedio de alquiler mes a mes (enero 2020 - mes actual 2021/22)
data_properati_CABA_alq %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: Vemos que los valores promedio están bastante elevados para lo que son los alquileres en CABA, incluso luego de aumentar por el efecto de la ley.
summary(data_properati_CABA_alq$precio)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3809 25000 35000 99695 65000 15073856Nos guardamos el corte del tercer quintil para omitir los valores extremos del análisis
variableprecioCABAalq <- summary(data_properati_CABA_alq$precio)
Q3CABAalq <- variableprecioCABAalq[5]
Q3CABAalq## 3rd Qu.
## 65000Elpromedio se ve fuertemente impactado por los valores extremos. Intentemos realizarlo cortando por el tercer quintil o utilizando la mediana
data_properati_CABA_alq %>%
filter(precio<Q3CABAalq) %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+500, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: Vemos que la tendencia ascendente del precio promedio es menos abrupta en entre Jul 2020 y oct 2020, a partir de alli la tendencia es mas pronunciada.
Hacemos el análisis utilizando la mediana
data_properati_CABA_alq %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: el salto ascendente en el precio promedio se da justo antes de Oct del 2 .
Sumamos el corte por el Q3
data_properati_CABA_alq %>%
filter(precio<Q3CABAalq) %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: Tiene mayor correlación con los precios que hemos visto y vemos en el mercado de alquileres de Capital Federal.
Analizar el costo de alq x m2
data_properati_CABA_alq %>%
group_by(fecha) %>%
summarise(preciopromxm2=round(median(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=preciopromxm2))+
geom_point(aes(fecha, y=preciopromxm2))+
geom_text(aes(fecha, y=preciopromxm2+25, label=preciopromxm2), size=3)+
labs(title = "Evolución de la variable precio por m2 - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: Vemos una tendencia ascendente a partir de agosto 2020, se mueve en la misma tendencia.
Qué sucede si cortamos la muestra en Q3
data_properati_CABA_alq %>%
filter(precio<Q3CABAalq) %>%
group_by(fecha) %>%
summarise(preciopromxm2=round(median(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=preciopromxm2))+
geom_point(aes(fecha, y=preciopromxm2))+
geom_text(aes(fecha, y=preciopromxm2+25, label=preciopromxm2), size=3)+
labs(title = "Evolución de la variable precio por m2 - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: El precio por m2 se mueve en la misma tendencia.
Abrir el análisis de precio de alquiler por Barrio
preciopromalq <- data_properati_CABA_alq %>%
filter(precio<Q3CABAalq) %>%
st_set_geometry(NULL) %>%
group_by(barrio) %>%
summarise(preciopromalq=median(precio))
preciopromalqxbarrio <- left_join(barrios, preciopromalq, by = "barrio")
ggplot()+
geom_sf(data=preciopromalqxbarrio, aes(fill=preciopromalq)) +
geom_sf_label(data=barrios, aes(label = barrio), size=4) +
scale_fill_distiller(palette = "Spectral") +
theme_light()+
labs(title="Precio promedio por Barrio en CABA - 2020-2021",
subtitle = "Fuente: Properati")+
theme_void()## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Conclusiones del gráfico: el corredor norte hasta puerto madero como lider se caracterizan por tener el mayor precio promedio de alquiler, como lo fue historicamente
Abrir dicho análisis por Barrio, fasetando por mes y año
preciopromalq <- data_properati_CABA_alq %>%
filter(precio<Q3CABAalq) %>%
st_set_geometry(NULL) %>%
group_by(barrio, fecha) %>%
summarise(preciopromalq=median(precio))## `summarise()` has grouped output by 'barrio'. You can override using the `.groups` argument.preciopromalqxbarrio <- left_join(barrios, preciopromalq, by = "barrio")
ggplot()+
geom_sf(data=preciopromalqxbarrio, aes(fill=preciopromalq)) +
geom_sf_label(data=barrios, aes(label = barrio), size=1) +
scale_fill_distiller(palette = "Spectral") +
theme_light()+
labs(title="Precio promedio por Barrio en CABA - 2020-2021",
subtitle = "Fuente: Properati")+
theme_void()+facet_wrap(~fecha)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Conclusiones del gráfico: Puerto madero fue perdiendo valor y zona oeste y sur de los barrios de la capital se fueron encareciendo, equiparandose con el corredor norte. La diferencia no es tan marcada como antes.
Evolución del Mercado Inmobiliario de Capital
Gráfico de barras con cantidad de publicaciones mes a mes (enero 2020 - mes actual 2021/22)
ggplot(data_properati_CABA)+
geom_bar(aes(x=fecha))
Notamos un muy leve decenso en la cantidad de publicaciones, más allá del pico de abril del 2021
Comparación alquileres vs ventas
ggplot(data_properati_CABA)+
geom_bar(aes(x=fecha, fill = operation_type))
Conclusiones del gráfico:
Evolución del Mercado de venta de inmuebles
Gráfico de líneas con el precio promedio de venta mes a mes (enero 2020 - mes actual 2021/22)
variableprecioCABAventa <- summary(data_properati_CABA_venta$precio)
Q3CABAventa <- variableprecioCABAventa[5]
Q3CABAventa## 3rd Qu.
## 40194480data_properati_CABA_venta %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Sumamos nuestro corte en Q3
data_properati_CABA_venta %>%
filter(precio<Q3CABAventa) %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico:
Veamos qué pasa con la mediana
data_properati_CABA_venta %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Cortamos en Q3
data_properati_CABA_venta %>%
filter(precio<Q3CABAventa) %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+100000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico:
Analizar el costo de venta x m2
data_properati_CABA_venta %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
data_properati_CABA_venta %>%
filter(precio<Q3CABAventa) %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico:
Del mismo modo que lo anterior, veamos qué pasa con la mediana
data_properati_CABA_venta %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+5000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico:
Abrir el análisis de precio de venta por Barrio
preciopromvta <- data_properati_CABA_venta %>%
filter(precio<Q3CABAventa) %>%
st_set_geometry(NULL) %>%
group_by(barrio, fecha) %>%
summarise(preciopromvta=median(precio))## `summarise()` has grouped output by 'barrio'. You can override using the `.groups` argument.preciopromvtaxbarrio <- left_join(barrios, preciopromvta, by = "barrio")
ggplot()+
geom_sf(data=preciopromvtaxbarrio, aes(fill=preciopromvta)) +
geom_sf_label(data=barrios, aes(label = barrio), size=4) +
scale_fill_distiller(palette = "Spectral") +
theme_light()+
labs(title="Precio promedio por Barrio en CABA - 2020-2021",
subtitle = "Fuente: Properati")+
theme_void()## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Conclusiones del gráfico:
Abrir dicho análisis por Barrio, fasetando por mes y año
preciopromvta <- data_properati_CABA_venta %>%
filter(precio<Q3CABAventa) %>%
st_set_geometry(NULL) %>%
group_by(barrio, fecha) %>%
summarise(preciopromvta=median(precio))## `summarise()` has grouped output by 'barrio'. You can override using the `.groups` argument.preciopromvtaxbarrio <- left_join(barrios, preciopromvta, by = "barrio")
ggplot()+
geom_sf(data=preciopromvtaxbarrio, aes(fill=preciopromvta)) +
geom_sf_label(data=barrios, aes(label = barrio), size=1) +
scale_fill_distiller(palette = "Spectral") +
theme_light()+
labs(title="Precio promedio por Barrio en CABA - 2020-2021",
subtitle = "Fuente: Properati")+
theme_void()+facet_wrap(~fecha)## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Analizar el costo de alq x m2
data_properati_CABA_alq %>%
group_by(fecha) %>%
summarise(precioprom=round(mean(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+50, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico:
Del mismo modo que lo anterior, veamos qué pasa con la mediana
data_properati_CABA_alq %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precioxm2))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+50, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio en ventas - Capital Federal 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico:
Evolución de las demás Provincias
Gráfico de barras con cantidad de publicaciones por Provincia
ggplot(data_properati_non_CABA)+
geom_bar(aes(x= l2, fill = l2))+
coord_flip()
Conclusiones del gráfico: Como se puede observar, las provincias con mayor cantidad de publicaciones son PBA (con primacía de la Zona Norte del GBA), Santa Fe (que supera las 20.000 publicaciones) y Córdoba (con aproximadamente 15.000 publicaciones).
Unificación de GBA
data_properati_non_CABA$l2 <- as.character(data_properati_non_CABA$l2)
data_properati_non_CABA$l2[data_properati_non_CABA$l2 == "Buenos Aires Interior"] <- "Provincia de Buenos Aires"
data_properati_non_CABA$l2[data_properati_non_CABA$l2 == "Buenos Aires Costa Atlántica"] <- "Provincia de Buenos Aires"
data_properati_non_CABA$l2[data_properati_non_CABA$l2 == "Bs.As. G.B.A. Zona Sur"] <- "Provincia de Buenos Aires"
data_properati_non_CABA$l2[data_properati_non_CABA$l2 == "Bs.As. G.B.A. Zona Oeste"] <- "Provincia de Buenos Aires"
data_properati_non_CABA$l2[data_properati_non_CABA$l2 == "Bs.As. G.B.A. Zona Norte"] <- "Provincia de Buenos Aires"
data_properati_non_CABA$l2 <- as.factor(data_properati_non_CABA$l2)
summary(data_properati_non_CABA)## start_date end_date created_on
## Min. :2020-05-24 Min. :2020-05-24 Min. :2020-05-24 00:00:00
## 1st Qu.:2020-09-18 1st Qu.:2020-10-25 1st Qu.:2020-09-18 00:00:00
## Median :2020-11-27 Median :2021-04-07 Median :2020-11-27 00:00:00
## Mean :2020-12-09 Mean :3132-06-02 Mean :2020-12-09 21:07:15
## 3rd Qu.:2021-03-15 3rd Qu.:2021-06-05 3rd Qu.:2021-03-15 00:00:00
## Max. :2021-06-17 Max. :9999-12-31 Max. :2021-06-17 00:00:00
##
## l1 l2 l3
## Argentina:153887 Provincia de Buenos Aires:105577 Rosario :18384
## Santa Fe : 21283 Tigre :15959
## Córdoba : 14810 Córdoba : 9868
## Neuquén : 3153 La Plata: 9660
## Tucumán : 3038 Pilar : 8597
## Río Negro : 1430 (Other) :87379
## (Other) : 4596 NA's : 4040
## l4 l5 l6
## Nordelta : 5382 BarrioPortezuelo : 425 Mode:logical
## La Plata : 4631 Barrio Los Alisos: 294 NA's:153887
## Olivos : 2660 Barrio El Golf : 289
## Nueva Córdoba: 2589 Barrio Los Sauces: 167
## Martínez : 1879 Barrio La Alameda: 153
## (Other) :60087 (Other) : 892
## NA's :76659 NA's :151667
## rooms bedrooms bathrooms surface_total
## Min. : 1.000 Min. : 0.000 Min. : 1.000 Min. : -136.0
## 1st Qu.: 2.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 53.0
## Median : 3.000 Median : 2.000 Median : 1.000 Median : 94.0
## Mean : 3.353 Mean : 2.378 Mean : 1.776 Mean : 197.4
## 3rd Qu.: 4.000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 200.0
## Max. :40.000 Max. :123.000 Max. :20.000 Max. :175000.0
## NA's :29129 NA's :21385 NA's :10114 NA's :53578
## surface_covered price currency price_period
## Min. : 1 Min. : 0 ARS: 36464 Mensual:153887
## 1st Qu.: 60 1st Qu.: 37000 USD:117423
## Median : 135 Median : 98000
## Mean : 2032 Mean : 386164
## 3rd Qu.: 535 3rd Qu.: 210000
## Max. :111111111 Max. :3100000000
##
## property_type operation_type Cotiz_dolar precio
## Casa :71054 Alquiler: 43685 Min. :112.6 Min. : 0
## Departamento:74713 Venta :110202 1st Qu.:123.4 1st Qu.: 408548
## PH : 8120 Median :134.1 Median : 12188088
## Mean :135.8 Mean : 20701770
## 3rd Qu.:149.9 3rd Qu.: 26256038
## Max. :157.3 Max. :3714170625
##
## precioxm2 fecha año_mes
## Min. : 0 Min. :2020-05-01 Length:153887
## 1st Qu.: 1562 1st Qu.:2020-09-01 Class :character
## Median : 118188 Median :2020-11-01 Mode :character
## Mean : 157933 Mean :2020-11-26
## 3rd Qu.: 229653 3rd Qu.:2021-03-01
## Max. :86168610 Max. :2021-06-01
##
## geometry gid entidad objeto
## POINT :153887 Min. :290 Min. :0 Length:153887
## epsg:4326 : 0 1st Qu.:290 1st Qu.:0 Class :character
## +proj=long...: 0 Median :290 Median :0 Mode :character
## Mean :290 Mean :0
## 3rd Qu.:290 3rd Qu.:0
## Max. :290 Max. :0
##
## fna gna nam fdc
## Length:153887 Length:153887 Length:153887 Length:153887
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## sag
## Length:153887
## Class :character
## Mode :character
##
##
##
## Seleccionar las 3 provincias con mayor volumen de publicaciones
ggplot(data_properati_non_CABA)+
geom_bar(aes(x= l2, fill = l2))+
coord_flip()
Conclusiones del gráfico: En este gráfico observamos que, si bien son 3 las provincias que se destacan por la cantidad de publicaciones, la Provincia de Buenos Aires (en este gráfico visualizada en su conjunto) supera ampliamente a las demás, alcanzando un total de más de 100.000 publicaciones.
Provincia 1 - GBA
Gráfico de líneas con el precio promedio de alquiler mes a mes (enero 2020 - mes actual 2021/22)
data_properati_non_CABA %>%
filter(l2 == "Provincia de Buenos Aires") %>%
filter(operation_type == "Alquiler") %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+10000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio de alquiler - Provincia de Buenos Aires 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: En relación a la evolución de la variable precio de alquiler en la Provincia de Buenos Aires 2020-2021, podemos observar que el pico máximo se alcanza en el segundo semestre del año 2020. Hacia principios de 2021 empieza a descender aunque no recupera los valores bajos del primer semestre del año 2020 y mantiene una estabilidad en los meses siguientes.
Comparación alquileres vs ventas
ggplot(data_properati_non_CABA %>% filter(l2 == "Provincia de Buenos Aires"))+
geom_bar(aes(x=fecha, fill = operation_type))
Conclusiones del gráfico: En todos los meses analizados se observa una mayor cantidad de propiedades a la venta que en alquiler.
Gráfico de líneas con el precio promedio de venta mes a mes (enero 2020 - mes actual 2021/22)
data_properati_non_CABA %>%
filter(l2 == "Provincia de Buenos Aires") %>%
filter(operation_type == "Venta") %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio de venta - Provincia de Buenos Aires 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: El gráfico nos muestra que luego de subas y bajas reiteradas de los precios de venta en la Provincia de Buenos Aires, desde enero de 2021 comienza cierta estabilidad de los valores.
Provincia 1 - Córdoba
Gráfico de líneas con el precio promedio de alquiler mes a mes (enero 2020 - mes actual 2021/22)
data_properati_non_CABA %>%
filter(l2 == "Córdoba") %>%
filter(operation_type == "Alquiler") %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio de alquiler - Provincia de Córdoba - 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: El valor más elevado se encuentra promediando el año 2021.
Comparación alquileres vs ventas
ggplot(data_properati_non_CABA %>% filter(l2 == "Córdoba"))+
geom_bar(aes(x=fecha, fill = operation_type))
Conclusiones del gráfico: Se observa una mayor cantidad de propiedades en venta que en alquiler en todos los meses de estudio.
Gráfico de líneas con el precio promedio de venta mes a mes (enero 2020 - mes actual 2021/22)
data_properati_non_CABA %>%
filter(l2 == "Córdoba") %>%
filter(operation_type == "Venta") %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio de venta - Provincia de Córdoba 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: En el año 2020 se pueden observar subas y bajas reiteradas en los precios de venta. En el año 2021 los valores del precio de venta en la Provincia de Córdoba empiezan a subir hacia principio de año para descender en el mes de abril y luego volver a aumentar hacia mitad del año 2021.
Provincia 1 - Santa Fe
Gráfico de líneas con el precio promedio de alquiler mes a mes (enero 2020 - mes actual 2021/22)
data_properati_non_CABA %>%
filter(l2 == "Santa Fe") %>%
filter(operation_type == "Alquiler") %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio de alquiler - Provincia de Santa Fe - 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: Luego del primer semestre del año 2020, el precio de los alquileres comienza a aumentar hasta lograr una estabilidad hacia los últimos meses del mismo año. Posteriormente vuelve a ascender en los cinco primeros meses del año 2021 y después experimenta una baja.
Comparación alquileres vs ventas
ggplot(data_properati_non_CABA %>% filter(l2 == "Santa Fe"))+
geom_bar(aes(x=fecha, fill = operation_type))
Conclusiones del gráfico: Se observa, como en las provincias analizadas anteriormente, que hay una mayor cantidad de publicaciones de propiedades a la venta por sobre las de alquiler.
Gráfico de líneas con el precio promedio de venta mes a mes (enero 2020 - mes actual 2021/22)
data_properati_non_CABA %>%
filter(l2 == "Santa Fe") %>%
filter(operation_type == "Venta") %>%
group_by(fecha) %>%
summarise(precioprom=round(median(precio))) %>%
ggplot() +
geom_line(aes(fecha, y=precioprom))+
geom_point(aes(fecha, y=precioprom))+
geom_text(aes(fecha, y=precioprom+1000, label=precioprom), size=3)+
labs(title = "Evolución de la variable precio de venta - Provincia de Santa Fe 2020 - 2021",
subtitle = "Fuentes: PROPERATI")## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Conclusiones del gráfico: En relación a los precios de venta, se observa un pico hacia finales del 2020 con una gran baja de los mismos en enero de 2021 para luego volver a subir y descender hacia abril que finalmente hay un aumento considerable de los mismos para luego lograr cierta estabilidad.
Conclusiones Finales del Análisis: Como impactó la Nueva Ley de Alquileres en el Mercado Inmobiliario